基于一维卷积循环神经网络的雷达辐射源信号识别

针对人工提取雷达辐射源信号特征不完备、时效性低等问题，提出一种基于一维卷积神经网络和双向门控循环单元的识别方法.首先，提取信号的模糊函数主脊并进行去噪处理；其次，利用一维卷积神经网络学习模糊函数主脊的内在抽象特征；然后引入双向门控循环单元对一维卷积神经网络提取到的特征进行再处理；最后，将特征映射到特征空间并通过Softmax分类器进行分类识别.实验结果表明，该方法在信噪比为0 dB时能保持99.67%的识别率，即使在-6 dB环境中识别率仍能达到90%左右，证实了该方法的有效性和在低信噪比下的稳定性.

Radar emitter signal recognition based on one-dimensional convolutional recurrent neural network

Aiming at the problem of incomplete features and low timeliness in artificial extraction of radar emitter signal, a novel recognition method is proposed based on one-dimension convolutional neural network and bidirectional gated recurrent unit. First, the main ridge of ambiguity function is extracted and denoised, then one-dimensional convolutional neural network is used to learn the intrinsic abstract characteristics of the main ridge of ambiguity function. The features extracted from the one-dimensional convolutional neural network are reprocessed by introducing the bidirectional gated recurrent unit. Finally, a deep neural network is constructed to map features to feature space and the classifier is Softmax. The results show that the proposed method can maintain 99.67% recognition rate when the SNR is 0 dB, and the recognition rate can still reach about 90% even in the -6 dB environment, which demonstrates the effectiveness and stability of the method at low SNR.